Alcohol’s role in our lives extends beyond a mere drink. It mirrors alcoholism, cultural facets, and economic dynamics. This blog delves into global alcohol consumption, spotlighting beer, spirits, and wine. These choices echo traditions, camaraderie, and refinement. Beer unites at gatherings, spirits offer mystique, and wine embodies elegance. From local pubs to international soirées, alcohol reflects diverse human experiences. This exploration unravels trends and stories, showcasing the interplay of culture and economics in our relationship with alcohol.
image source: Wikipedia
Data description
This data shows the beer, spirit and wine consumption and total litres of pure alcohol by country. The data source is from Thomas Mock (2022). Tidy Tuesday: A weekly data project aimed at the R ecosystem https://github.com/rfordatascience/tidytuesday.
Variables:
country : global country name.
beer_servings : consumption of beer.
spirit_servings : consumption of spirit.
wine-servings : consumption of wine.
total_litres_of_pure_alcohol : the total (sum of recorded and unrecorded alcohol) amount of alcohol consumed per person (15 years of age or older) over a calendar year, in litres of pure alcohol, adjusted for tourist consumption. (The World Bank Group,2022)
map <-map_data("world") map$region <-gsub("Russia", "Russian Federation", map$region)map_data <- map %>%left_join(alcohol, by =c("region"="country")) p1<-ggplot(map_data, aes(x = long, y = lat, group = group, text =sprintf("region: %s<br>total litres of pure alcohol: %s", region, total_litres_of_pure_alcohol)))+geom_polygon( aes(fill = total_litres_of_pure_alcohol), color ="white")+scale_fill_gradient(low ="#f7fcfd", high ="#4d004b",na.value ="grey",breaks =c(0,2,4,6,8,10,12,14),labels =c(0,2,4,6,8,10,12,14))+theme_void()+theme(panel.grid =element_blank(),axis.line.x =element_blank(),axis.line.y =element_blank())+labs(fill ="Litres",title ="Global alcohol consumption")
Code
ggplotly(p1,tooltip ="text")
Figure1: Global alcohol consumption (total litres of pure alcohol). Note: NA value: grey. Litres: total litres of pure alcohol
From Figure1, we could see that alcohol consumption in Europe and Australia is almost higher than 10. By contrast, alcohol consumption in Africa is close to zero. And the consumption in South America and North America is approximately 8, while it in Asia is around 5.
Global alcohol consumption of beer, spirit and wine
Code
alcohol_table <-tibble('alcohol servings'=c("beer servings","spirit servings","wine servings"),mean =c(round(mean(alcohol$beer_servings),2),round(mean(alcohol$spirit_servings),2),round(mean(alcohol$wine_servings),2) ),median =c(median(alcohol$beer_servings),median(alcohol$spirit_servings),median(alcohol$wine_servings)),sum =c(sum(alcohol$beer_servings),sum(alcohol$spirit_servings),sum(alcohol$wine_servings)) ) alcohol_table %>% knitr::kable(caption ="Table1: Mean, median and sum of beer servings, spirit servings and wine servings") %>%kable_styling()
Table1: Mean, median and sum of beer servings, spirit servings and wine servings
alcohol servings
mean
median
sum
beer servings
106.16
76
20489
spirit servings
80.99
56
15632
wine servings
49.45
8
9544
From Table1, one thing we need to note that is we cannot compare the consumption by alcohol servings without a unit. We could easily know that beer consumption in a country is normally around 106 and the total beer consumption is around 20 thousand, and the average spirit consumption is around 80 and the total spirit consumption is around 15 thousand, and the average wine consumption is approximately 50 and the total wine consumption is around 9 thousand.
Global beer consumption
Code
p2<-ggplot(map_data, aes(x = long, y = lat, group = group, text =sprintf("region: %s<br>beer servings: %s", region, beer_servings)))+geom_polygon( aes(fill = beer_servings), color ="white")+scale_fill_gradient(low ="#fff7f3", high ="#49006a",na.value ="grey",breaks =c(0,50,100,150,200,250,300,350,400),labels =c(0,50,100,150,200,250,300,350,400))+theme_void()+theme(panel.grid =element_blank(),axis.line.x =element_blank(),axis.line.y =element_blank())+labs(fill ="Servings",title ="Beer global alcohol consumption")
Code
ggplotly(p2, tooltip ="text")
Figure2: Global beer consumption. Note: NA value: grey. Servings: beer servings
See Figure2, beer consumption in northern Africa is almost under 50, and the consumption in southwestern Africa is almost high, with most countries over 200, and even near 350. Beer servings in Europe, North America, South America and Australia are almost higher than 200, especially consumption in Europe, some countries exceed 300, while servings in Asia are all approximately under 100.
Global spirit consumption
Code
p3<-ggplot(map_data, aes(x = long, y = lat, group = group, text =sprintf("region: %s<br>spirit servings: %s", region, spirit_servings)))+geom_polygon( aes(fill = spirit_servings), color ="white")+scale_fill_gradient(low ="#fff7f3", high ="#49006a",na.value ="grey" ,breaks =c(0,50,100,150,200,250,300,350,400),labels =c(0,50,100,150,200,250,300,350,400))+theme_void()+theme(panel.grid =element_blank(),axis.line.x =element_blank(),axis.line.y =element_blank())+labs(fill ="Servings",title ="Spirit global alcohol consumption",caption ="NA value: grey; Servings: spirit servings")
Code
ggplotly(p3, tooltip ="text")
Figure3: Global spirit consumption. Note: NA value: grey. Servings: spirit servings
In Figure3, the consumption of spirit is highest in Eastern Europe, for example, Russian Federation and Belarus consume more than 300. And the consumption in Asia is around 200, in Africa, it is almost close to zero.
Global wine consumption
Code
p4 <-ggplot(map_data, aes(x = long, y = lat, group = group, text =sprintf("region: %s<br>wine servings: %s", region, wine_servings)))+geom_polygon( aes(fill = wine_servings), color ="white")+scale_fill_gradient(low ="#fff7f3", high ="#49006a",na.value ="grey",breaks =c(0,50,100,150,200,250,300,350,400),labels =c(0,50,100,150,200,250,300,350,400))+theme_void()+theme(panel.grid =element_blank(),axis.line.x =element_blank(),axis.line.y =element_blank())+labs(fill ="Servings",title ="Wine global alcohol consumption",caption ="NA value: grey; Servings: wine servings")
Code
ggplotly(p4, tooltip ="text")
Figure4: Global wine consumption. Note: NA value: grey. Servings: wine servings
From Figure4, it is easy to see that global wine consumption is not high, except in southern South America, Australia and western Europe, including Argentina, France and Australia, where the consumption is greater than 200.
Dataset
More data can be found here.
Code
alcohol <- alcohol %>%rename('beer servings'= beer_servings,'spirit servings'= spirit_servings,'wine servings'= wine_servings,'total litres of pure alcohol'= total_litres_of_pure_alcohol)DT::datatable(alcohol, options =list(pageLength =5), caption ="Alcohol consumption by country",text_spec(alcohol, font_size ="small") )
R Core Team (2022). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/.
Sievert, C (2020). Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC. https://plotly-r.com.
Wickham, H, M Averick, J Bryan, W Chang, LD McGowan, R François, G Grolemund, A Hayes, L Henry, J Hester, M Kuhn, TL Pedersen, E Miller, SM Bache, K Müller, J Ooms, D Robinson, DP Seidel, V Spinu, K Takahashi, D Vaughan, C Wilke, K Woo, and H Yutani (2019). Welcome to the tidyverse. Journal of Open Source Software 4(43), 1686.
Xie, Y (2022). knitr: A General-Purpose Package for Dynamic Report Generation in R. R package version1.38. https://yihui.org/knitr/.
Xie, Y, J Cheng, and X Tan (2022). DT: A Wrapper of the JavaScript Library ‘DataTables’. R package version 0.24. https://CRAN.R-project.org/package=DT.
